Indian Journal of Science and Technology, Vol 8(26), DOI: 10.17485/ijst/2015/v8i26/77179, October 2015 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 * Author for correspondence 1. Introduction Te high pace increase of Cloud computing has caused the establishment of the large scale data centres comprising thousands of computing nodes that requires huge energy consumption. Tere is an inevitable need of energy efcient cloud computing techniques. In addition, the high pace increase in the number of users in Cloud computing has raised varied challenges for Cloud service provider to ensure optimal Quality of Service (QoS) and reliability. Employing live migration 1 VM consolidation can be performed to leverage the fne-grained instabilities in the workload that ultimately can maintain minimal active hosts to conserve energy. In general, the dynamic VM consolidation comprises two fundamental processes; VM migration from underutilized physical machine and ofoading VMs from PMs, when it sufers from overloading to avoid any possible performance degradation or SLA violation. In this process, the idle nodes are switched of to eliminate the static or active mode power consumption and as per requirements the nodes (PMs) are reactivated to accommodate migrated VMs. Te modern Cloud infrastructures employ virtualization for optima resource utilization, minimal computational cost and energy efcient computing. Virtualization enables Cloud service providers to deal Abstract Background: The increase in Cloud applications have demanded efficient cloud computing systems like Virtual Machine (VM) consolidation that intends to facilitate optimal resource utilization, energy conservation and quality of service. Methods: In this paper, an evolutionary computing technique called Adaptive Genetic Algorithm (A-GA) has been proposed for VM consolidation that encompasses under load and overload utilization detection, VM selection and placement, where the modified robust local regression and interquartile range schemes estimate the dynamic CPU utilization threshold for overload detection, minimum migration time works as VM selection policy, while A-GA optimizes VM placement across network to reduce energy consumption and SLA violation. Findings: PlanetLab Cloud benchmark data based simulation results confirms that the proposed VM consolidation scheme exhibits better than other existing approaches such as Ant Colony Optimization (ACO), Static Threshold (THR), Local Regression (LR), Conventional Inter Quartile Range (IQR) and Median Absolute Deviation (MAD) based virtualization schemes. The proposed system has exhibited minimal host shutdown, VM migration, energy consumption and SLA violation as compared to other existing approaches. Applications: Thus, the efficiency of the proposed VM consolidation scheme signifies that it can be a potential VM consolidation solution for large scale Cloud data centers. Keywords: Adaptive Genetic Algorithm, Evolutionary Computing, Resource Utilization, VM Consolidation An Evolutionary Computing based Energy Efcient VM Consolidation Scheme for Optimal Resource Utilization and QoS Assurance Perla Ravi Theja 1* and S. K. Khadar Babu 2 1 School of Computing, Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, India; ravithejaperla9048@gmail.com 2 School of Advanced Sciences, VIT University, Vellore - 632 014, Tamil Nadu, India; khadar.babu36@gmail.com